Leaving hip rotation out of a conventional 3D gait model improves discrimination of pathological gait in cerebral palsy: A novel neural network analysis |
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| Affiliation: | 1. Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, L3 3AF, United Kingdom;2. Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, USA;3. Gillette Children’s Specialty Healthcare, St. Paul, USA;1. Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, 3-137 Cambridge, MA 02139, USA;2. Department of Mechanical and Process Engineering, ETH Zurich, Switzerland;3. Department of Matter, Transport and Energy, Arizona State University, USA;4. Department of Health Sciences and Technology, ETH Zurich, Switzerland;5. Faculty of Medicine, University of Zurich, Switzerland;6. Department Neurology and Division Rehabilitative Medicine, University of Maryland, School of Medicine, USA;7. Department of Physical Medicine and Rehabilitation, Fujita Health University, Japan;8. Institute of Neuroscience, University of Newcastle, UK;1. Graduate School of Human Development and Environment, Kobe University, 3-11 Tsurukabuto, Nada-ku, Kobe-shi, Hyogo, 657-8501, Japan;2. Department of Rehabilitation, Midorigaoka Hospital, 3-13-1, Makamicho, Takatsuki-shi, Osaka, 569-1121, Japan;1. School of Exercise Science, Australian Catholic University, Daniel Mannix Building, 17 Young St, Fitzroy, VIC 3065, Australia;2. Centre for Sport Research, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia;3. Sport Performance Research Institute New Zealand, Auckland University of Technology, 17 Antares Place, Rosedale, Auckland 0632, New Zealand;4. Department of Epidemiology and Preventive Medicine, Monash University, 553 St Kilda Road, Melbourne VIC 3004, Australia;5. Nicholas Institute of Sports Medicine and Athletic Trauma, Lenox Hill Hospital, 210 East 64th St, 5th Floor, New York, NY 10065, USA;6. Harkness Center for Dance Injuries, NYU Langone Health, 614 2ndAv, Suite G, 2nd Floor, New York, NY 10016, USA |
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| Abstract: | BackgroundComplex clinical gait analysis results can be expressed as single number gait deviations by applying multivariate processing methods. The original Movement Deviation Profile (MDP) quantifies the deviation of abnormal gait using the most trusted nine dynamic joint angles of lower limbs.Research questionWhich subset of joint angles maximises the ability of the MDP to separate abnormal gait from normality? What is the effect of using the best subset in a large group of patients, and in individuals?MethodsA self-organising neural network was trained using normal gait data from 166 controls, and then the MDP of 1923 patients with cerebral palsy (3846 legs) was calculated. The same procedure was repeated with 511 combinations of the nine joint angles. The standardised distances of abnormal gait from normality were then calculated as log-transformed Z-scores to select the best combination. A mixed design ANOVA was used to assess how removing the least discriminating angle improved the separation of patients from controls. The effect of using the optimal subset of angles was also quantified for each individual leg by comparing the change in MDP to the independent FAQ levels of patients.ResultsRemoval of hip rotation significantly (p<0.0005) increased the separation of the patient group from normality (ΔZ-score 0.24) and also at FAQ levels 7-10 (ΔZ-score 0.38, 0.27, 0.22, 0.14). The MDP of individual patients changed in a wider range of -4.65 to 1.12 Z-scores and their change matched their independent FAQ scores, with less functional patients moving further from, and more functional patients moving closer to normality.SignificanceIn existing gait databases we recommend excluding hip rotation from data used to calculate the MDP. Alternatively, the calculation of hip rotation can be improved by post-hoc correction, but the ultimate solution is to use more accurate and reliable models of hip rotation. |
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| Keywords: | Gait Movement deviation profile Sensitivity analysis Neural network Hip rotation |
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